Load packages

Set up Environment & Import Data

Note - either import pre-made cestfcmat df or make here.

Remove Values with poor CEST coverage

CEST Density Plots

CEST-FC Scatterplot

## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2588 -2.0385 -0.6774  1.7723  8.6459 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  12.6722     3.8178   3.319  0.00193 **
## age          -0.1067     0.1637  -0.652  0.51809   
## X52           1.1863     6.8918   0.172  0.86420   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.961 on 40 degrees of freedom
## Multiple R-squared:  0.01052,    Adjusted R-squared:  -0.03895 
## F-statistic: 0.2127 on 2 and 40 DF,  p-value: 0.8093
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0132 -2.5455 -0.6675  1.9156  7.9804 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  14.3819     5.8545   2.457   0.0224 *
## age          -0.1807     0.2511  -0.720   0.4793  
## X52          -5.0122    12.2635  -0.409   0.6867  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.554 on 22 degrees of freedom
## Multiple R-squared:  0.03158,    Adjusted R-squared:  -0.05646 
## F-statistic: 0.3587 on 2 and 22 DF,  p-value: 0.7026
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1957 -1.2998 -0.6481  0.7029  3.8574 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 12.21480    4.31789   2.829   0.0127 *
## age         -0.09954    0.18761  -0.531   0.6035  
## X52          8.26362    7.09369   1.165   0.2622  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.98 on 15 degrees of freedom
## Multiple R-squared:  0.08308,    Adjusted R-squared:  -0.03918 
## F-statistic: 0.6795 on 2 and 15 DF,  p-value: 0.5218
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4955 -0.9337 -0.2379  0.6190  4.3369 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.25347    1.47231   8.323 5.29e-11 ***
## age         -0.12852    0.06386  -2.013   0.0496 *  
## X54         -0.18686    3.25395  -0.057   0.9544    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.33 on 50 degrees of freedom
## Multiple R-squared:  0.07741,    Adjusted R-squared:  0.04051 
## F-statistic: 2.098 on 2 and 50 DF,  p-value: 0.1334
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1648 -1.2398 -0.2511  0.7529  3.9739 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.68314    2.18742   5.798 3.15e-06 ***
## age         -0.15623    0.09443  -1.655    0.109    
## X54         -7.72392    5.29825  -1.458    0.156    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.489 on 28 degrees of freedom
## Multiple R-squared:  0.1561, Adjusted R-squared:  0.09579 
## F-statistic: 2.589 on 2 and 28 DF,  p-value: 0.09296
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0248 -0.6966 -0.2088  0.5622  2.5828 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.12341    1.63814   7.401 5.22e-07 ***
## age         -0.12331    0.07211  -1.710   0.1035    
## X54          6.77304    3.59133   1.886   0.0747 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9602 on 19 degrees of freedom
## Multiple R-squared:  0.2079, Adjusted R-squared:  0.1246 
## F-statistic: 2.494 on 2 and 19 DF,  p-value: 0.1092
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5601 -1.0399 -0.2810  0.8848  2.3923 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.18507    1.67247   5.492 3.32e-06 ***
## age         -0.04503    0.07213  -0.624    0.536    
## X55          1.38931    4.47119   0.311    0.758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.295 on 36 degrees of freedom
## Multiple R-squared:  0.01219,    Adjusted R-squared:  -0.04268 
## F-statistic: 0.2222 on 2 and 36 DF,  p-value: 0.8019
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3427 -1.0574 -0.3604  1.1319  2.2185 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  9.25943    2.44674   3.784  0.00109 **
## age         -0.05527    0.10234  -0.540  0.59487   
## X55          0.51147    6.48029   0.079  0.93784   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.403 on 21 degrees of freedom
## Multiple R-squared:  0.01438,    Adjusted R-squared:  -0.07948 
## F-statistic: 0.1532 on 2 and 21 DF,  p-value: 0.8589
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3412 -0.7946 -0.0431  0.4583  2.0823 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  9.06856    2.39852   3.781  0.00262 **
## age         -0.02758    0.10773  -0.256  0.80231   
## X55          2.39046    6.48127   0.369  0.71868   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.162 on 12 degrees of freedom
## Multiple R-squared:  0.0124, Adjusted R-squared:  -0.1522 
## F-statistic: 0.07535 on 2 and 12 DF,  p-value: 0.9279
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4606 -1.3294 -0.3652  0.9298  3.8331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.04873    1.85318   6.502 3.61e-08 ***
## age         -0.12654    0.08091  -1.564    0.124    
## X56          4.41771    4.91941   0.898    0.373    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.674 on 50 degrees of freedom
## Multiple R-squared:  0.05323,    Adjusted R-squared:  0.01536 
## F-statistic: 1.406 on 2 and 50 DF,  p-value: 0.2547
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5840 -1.7164 -0.6351  1.7558  3.6972 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.1863     2.9107   4.187 0.000254 ***
## age          -0.1264     0.1262  -1.002 0.324972    
## X56           3.9406     8.3633   0.471 0.641168    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.992 on 28 degrees of freedom
## Multiple R-squared:  0.04086,    Adjusted R-squared:  -0.02765 
## F-statistic: 0.5965 on 2 and 28 DF,  p-value: 0.5576
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7366 -0.9180 -0.0678  0.6953  2.3104 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.27202    2.09702   5.852 1.23e-05 ***
## age         -0.14727    0.09342  -1.576    0.131    
## X56          6.87563    5.40434   1.272    0.219    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.198 on 19 degrees of freedom
## Multiple R-squared:  0.1356, Adjusted R-squared:  0.04463 
## F-statistic: 1.491 on 2 and 19 DF,  p-value: 0.2504
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.02025 -0.48500  0.01973  0.54665  1.80700 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.72984    0.98977   9.830 3.54e-13 ***
## age         -0.09960    0.04331  -2.300   0.0258 *  
## X60          1.69106    2.32599   0.727   0.4707    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8959 on 49 degrees of freedom
## Multiple R-squared:  0.1004, Adjusted R-squared:  0.06365 
## F-statistic: 2.733 on 2 and 49 DF,  p-value: 0.07492
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0130 -0.7482  0.1332  0.6207  1.8194 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.77508    1.43762   6.104 1.38e-06 ***
## age         -0.05945    0.06135  -0.969    0.341    
## X60          0.61473    3.23851   0.190    0.851    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9669 on 28 degrees of freedom
## Multiple R-squared:  0.03461,    Adjusted R-squared:  -0.03435 
## F-statistic: 0.5018 on 2 and 28 DF,  p-value: 0.6108
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.34686 -0.50415  0.07637  0.54653  1.45607 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.57785    1.36154   8.504 1.02e-07 ***
## age         -0.18300    0.06191  -2.956  0.00846 ** 
## X60          5.47620    3.40743   1.607  0.12542    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7692 on 18 degrees of freedom
## Multiple R-squared:  0.3313, Adjusted R-squared:  0.257 
## F-statistic: 4.458 on 2 and 18 DF,  p-value: 0.02675
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6007 -0.6908 -0.2193  0.4912  3.0850 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.47980    1.27742   8.204 1.09e-10 ***
## age         -0.09559    0.05656  -1.690   0.0975 .  
## X69         -4.18832    3.17211  -1.320   0.1930    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.098 on 48 degrees of freedom
## Multiple R-squared:  0.1187, Adjusted R-squared:  0.08199 
## F-statistic: 3.233 on 2 and 48 DF,  p-value: 0.04818
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5209 -0.5371 -0.1061  0.3650  2.8851 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  10.9558     1.5936   6.875  1.8e-07 ***
## age          -0.1101     0.0705  -1.561   0.1297    
## X69          -7.0200     4.0909  -1.716   0.0972 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.075 on 28 degrees of freedom
## Multiple R-squared:  0.2059, Adjusted R-squared:  0.1492 
## F-statistic: 3.629 on 2 and 28 DF,  p-value: 0.03967
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6323 -0.8472 -0.2998  0.5146  2.2342 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.80830    2.21559   4.427 0.000369 ***
## age         -0.07663    0.09823  -0.780 0.446051    
## X69         -0.35016    5.27525  -0.066 0.947851    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.178 on 17 degrees of freedom
## Multiple R-squared:  0.04129,    Adjusted R-squared:  -0.0715 
## F-statistic: 0.3661 on 2 and 17 DF,  p-value: 0.6988
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.58080 -0.43055 -0.05936  0.47751  1.93996 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.21523    0.70318  14.527   <2e-16 ***
## age         -0.07630    0.03153  -2.420   0.0193 *  
## X73         -2.33010    2.13479  -1.091   0.2804    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6344 on 49 degrees of freedom
## Multiple R-squared:  0.1566, Adjusted R-squared:  0.1222 
## F-statistic:  4.55 on 2 and 49 DF,  p-value: 0.01539
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5927 -0.3389 -0.1474  0.4044  1.7995 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.86969    1.06284  10.227 5.85e-11 ***
## age         -0.10336    0.04652  -2.222   0.0345 *  
## X73         -4.03388    4.02491  -1.002   0.3248    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7256 on 28 degrees of freedom
## Multiple R-squared:  0.1956, Adjusted R-squared:  0.1381 
## F-statistic: 3.404 on 2 and 28 DF,  p-value: 0.04749
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8878 -0.2144 -0.0964  0.3731  0.8406 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.39995    0.81386  11.550 9.32e-10 ***
## age         -0.03662    0.03709  -0.988    0.336    
## X73         -2.15335    2.03985  -1.056    0.305    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4695 on 18 degrees of freedom
## Multiple R-squared:  0.1562, Adjusted R-squared:  0.06245 
## F-statistic: 1.666 on 2 and 18 DF,  p-value: 0.2168
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29616 -0.47340 -0.03904  0.35317  1.60225 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.93245    0.83449  10.704 2.61e-14 ***
## age         -0.05932    0.03685  -1.610    0.114    
## X74          3.48389    2.07714   1.677    0.100 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7546 on 48 degrees of freedom
## Multiple R-squared:  0.08639,    Adjusted R-squared:  0.04832 
## F-statistic: 2.269 on 2 and 48 DF,  p-value: 0.1144
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.13657 -0.54719  0.09621  0.36211  1.58776 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.558683   1.115445   6.776 2.81e-07 ***
## age         -0.001612   0.048791  -0.033    0.974    
## X74          2.711060   2.887418   0.939    0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7635 on 27 degrees of freedom
## Multiple R-squared:  0.0319, Adjusted R-squared:  -0.03981 
## F-statistic: 0.4449 on 2 and 27 DF,  p-value: 0.6455
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44979 -0.36496  0.06795  0.36518  1.20414 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.84892    1.19729   9.061 3.98e-08 ***
## age         -0.14067    0.05347  -2.631    0.017 *  
## X74          4.84150    2.84562   1.701    0.106    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7035 on 18 degrees of freedom
## Multiple R-squared:  0.3079, Adjusted R-squared:  0.231 
## F-statistic: 4.003 on 2 and 18 DF,  p-value: 0.03646
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9961 -0.4039  0.0059  0.4824  1.3567 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.66020    0.75368  11.491 1.22e-15 ***
## age         -0.05073    0.03287  -1.543    0.129    
## X77          1.76387    1.88187   0.937    0.353    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6899 on 50 degrees of freedom
## Multiple R-squared:  0.05662,    Adjusted R-squared:  0.01888 
## F-statistic:   1.5 on 2 and 50 DF,  p-value: 0.2329
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.00102 -0.26108  0.04178  0.45037  1.13691 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.508874   1.079211   6.958 1.45e-07 ***
## age         -0.002108   0.046734  -0.045    0.964    
## X77          4.262515   3.223054   1.323    0.197    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7381 on 28 degrees of freedom
## Multiple R-squared:  0.05889,    Adjusted R-squared:  -0.008329 
## F-statistic: 0.8761 on 2 and 28 DF,  p-value: 0.4275
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.18304 -0.22776  0.07674  0.27727  1.18595 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.99072    0.99518  10.039 4.94e-09 ***
## age         -0.10807    0.04378  -2.468   0.0232 *  
## X77          0.75436    2.12503   0.355   0.7265    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5986 on 19 degrees of freedom
## Multiple R-squared:  0.2435, Adjusted R-squared:  0.1639 
## F-statistic: 3.059 on 2 and 19 DF,  p-value: 0.07054
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5643 -0.8766  0.1701  0.9539  2.4067 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   4.2779     2.3648   1.809   0.0825 .
## age           0.1233     0.1050   1.174   0.2513  
## X78           9.5729     6.1412   1.559   0.1316  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.343 on 25 degrees of freedom
## Multiple R-squared:  0.1382, Adjusted R-squared:  0.06922 
## F-statistic: 2.004 on 2 and 25 DF,  p-value: 0.1559
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4806 -0.7151  0.1197  0.7220  1.1511 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  5.35563    2.86179   1.871   0.0859 .
## age          0.08657    0.13001   0.666   0.5181  
## X78          3.09607    5.01629   0.617   0.5486  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8821 on 12 degrees of freedom
## Multiple R-squared:  0.04871,    Adjusted R-squared:  -0.1098 
## F-statistic: 0.3073 on 2 and 12 DF,  p-value: 0.7411
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7119 -1.0611  0.3185  0.9461  2.5276 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  6.47228    5.08018   1.274    0.231
## age          0.02996    0.20795   0.144    0.888
## X78         27.08856   20.94504   1.293    0.225
## 
## Residual standard error: 1.705 on 10 degrees of freedom
## Multiple R-squared:  0.239,  Adjusted R-squared:  0.08682 
## F-statistic:  1.57 on 2 and 10 DF,  p-value: 0.2552
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8249 -0.5299 -0.0542  0.4267  1.7789 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.82728    0.90798   9.722 6.32e-13 ***
## age         -0.03759    0.03964  -0.948    0.348    
## X87          0.89204    2.31847   0.385    0.702    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8216 on 48 degrees of freedom
## Multiple R-squared:  0.02026,    Adjusted R-squared:  -0.02056 
## F-statistic: 0.4963 on 2 and 48 DF,  p-value: 0.6119
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6482 -0.4748 -0.1116  0.4754  1.9641 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.17641    1.26281   6.475 7.32e-07 ***
## age         -0.01016    0.05504  -0.185    0.855    
## X87          2.89021    3.97285   0.727    0.473    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8485 on 26 degrees of freedom
## Multiple R-squared:  0.02128,    Adjusted R-squared:  -0.05401 
## F-statistic: 0.2827 on 2 and 26 DF,  p-value: 0.7561
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.31086 -0.56337  0.07528  0.38087  1.58975 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.53883    1.37500   6.937  1.3e-06 ***
## age         -0.06772    0.06011  -1.127    0.274    
## X87          0.02106    2.95209   0.007    0.994    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8281 on 19 degrees of freedom
## Multiple R-squared:  0.06413,    Adjusted R-squared:  -0.03438 
## F-statistic: 0.651 on 2 and 19 DF,  p-value: 0.5328
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3157 -0.6779 -0.1836  0.3957  2.1509 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.30612    0.95656  10.774  1.6e-14 ***
## age         -0.07991    0.04177  -1.913   0.0616 .  
## X88          1.48745    2.57400   0.578   0.5660    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8591 on 49 degrees of freedom
## Multiple R-squared:  0.07062,    Adjusted R-squared:  0.03269 
## F-statistic: 1.862 on 2 and 49 DF,  p-value: 0.1662
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1253 -0.5351 -0.1004  0.3838  2.0579 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.57550    1.26056   8.389 3.99e-09 ***
## age         -0.08883    0.05449  -1.630    0.114    
## X88         -2.28791    4.74645  -0.482    0.634    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8603 on 28 degrees of freedom
## Multiple R-squared:  0.09213,    Adjusted R-squared:  0.02728 
## F-statistic: 1.421 on 2 and 28 DF,  p-value: 0.2584
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3441 -0.6390  0.1206  0.3982  1.8679 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.27690    1.59730   6.434 4.69e-06 ***
## age         -0.08133    0.07045  -1.154    0.263    
## X88          3.10607    3.38767   0.917    0.371    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9049 on 18 degrees of freedom
## Multiple R-squared:  0.08377,    Adjusted R-squared:  -0.01803 
## F-statistic: 0.8229 on 2 and 18 DF,  p-value: 0.455
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2622 -0.4497 -0.0623  0.4409  1.5304 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.64061    0.71943  13.400   <2e-16 ***
## age         -0.04911    0.03126  -1.571   0.1224    
## X91          3.30331    1.77885   1.857   0.0692 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6582 on 50 degrees of freedom
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.06473 
## F-statistic: 2.799 on 2 and 50 DF,  p-value: 0.07041
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.31263 -0.43830 -0.00099  0.34518  1.48773 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.51150    1.04885   9.069 7.96e-10 ***
## age         -0.04085    0.04562  -0.895    0.378    
## X91          2.73020    2.65505   1.028    0.313    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7089 on 28 degrees of freedom
## Multiple R-squared:  0.0747, Adjusted R-squared:  0.00861 
## F-statistic:  1.13 on 2 and 28 DF,  p-value: 0.3372
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.16974 -0.42055 -0.08608  0.40011  1.09914 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.08941    1.06879   9.440 1.32e-08 ***
## age         -0.07280    0.04668  -1.560   0.1353    
## X91          4.73494    2.59310   1.826   0.0836 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6105 on 19 degrees of freedom
## Multiple R-squared:  0.1845, Adjusted R-squared:  0.09861 
## F-statistic: 2.149 on 2 and 19 DF,  p-value: 0.1441
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82522 -0.55652 -0.03506  0.65108  2.14132 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.36764    1.04521   9.919  3.3e-13 ***
## age         -0.14585    0.04514  -3.231  0.00223 ** 
## X92          5.73249    2.41862   2.370  0.02185 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9197 on 48 degrees of freedom
## Multiple R-squared:  0.2373, Adjusted R-squared:  0.2055 
## F-statistic: 7.465 on 2 and 48 DF,  p-value: 0.001503
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8402 -0.5355  0.1245  0.4571  2.2789 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.82234    1.33699   7.347 6.67e-08 ***
## age         -0.12331    0.05774  -2.135   0.0419 *  
## X92          4.87911    3.05431   1.597   0.1218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8824 on 27 degrees of freedom
## Multiple R-squared:  0.2294, Adjusted R-squared:  0.1723 
## F-statistic: 4.019 on 2 and 27 DF,  p-value: 0.02967
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9210 -0.7479  0.2640  0.8638  1.1583 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.45835    1.87874   6.099 9.21e-06 ***
## age         -0.19178    0.08131  -2.359   0.0298 *  
## X92          7.97377    4.43078   1.800   0.0887 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.024 on 18 degrees of freedom
## Multiple R-squared:  0.2698, Adjusted R-squared:  0.1887 
## F-statistic: 3.326 on 2 and 18 DF,  p-value: 0.059
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4114 -0.2749  0.1166  0.4879  1.1598 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.39419    0.81834  10.258  1.4e-13 ***
## age         -0.05515    0.03544  -1.556    0.126    
## X93         -1.66943    1.68336  -0.992    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7228 on 47 degrees of freedom
## Multiple R-squared:  0.06429,    Adjusted R-squared:  0.02447 
## F-statistic: 1.615 on 2 and 47 DF,  p-value: 0.2098
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2404 -0.2390  0.1305  0.4251  1.4323 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.86776    1.12461   7.885 2.32e-08 ***
## age         -0.07938    0.04906  -1.618   0.1177    
## X93         -4.27348    2.19201  -1.950   0.0621 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7383 on 26 degrees of freedom
## Multiple R-squared:  0.1705, Adjusted R-squared:  0.1066 
## F-statistic: 2.671 on 2 and 26 DF,  p-value: 0.08809
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43050 -0.32536 -0.07651  0.35767  1.12994 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.49408    1.20488   7.050 1.41e-06 ***
## age         -0.05219    0.05135  -1.016    0.323    
## X93          2.60783    2.68974   0.970    0.345    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6813 on 18 degrees of freedom
## Multiple R-squared:  0.08634,    Adjusted R-squared:  -0.01518 
## F-statistic: 0.8505 on 2 and 18 DF,  p-value: 0.4437
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3536 -0.5691 -0.0912  0.6574  3.6698 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.60236    1.33164   6.460 7.83e-08 ***
## age         -0.01517    0.05729  -0.265    0.792    
## X96          0.37896    3.44385   0.110    0.913    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.125 on 43 degrees of freedom
## Multiple R-squared:  0.001927,   Adjusted R-squared:  -0.0445 
## F-statistic: 0.0415 on 2 and 43 DF,  p-value: 0.9594
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7823 -0.6640  0.2707  0.6610  1.8226 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.79091    1.54339   5.696 6.23e-06 ***
## age         -0.02522    0.06568  -0.384    0.704    
## X96         -3.42950    3.79385  -0.904    0.375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9607 on 25 degrees of freedom
## Multiple R-squared:  0.03216,    Adjusted R-squared:  -0.04527 
## F-statistic: 0.4153 on 2 and 25 DF,  p-value: 0.6646
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2184 -0.7637 -0.2319  0.8321  2.8872 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  10.5346     2.8169   3.740  0.00197 **
## age          -0.1031     0.1264  -0.816  0.42748   
## X96           9.5932     8.2828   1.158  0.26489   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.343 on 15 degrees of freedom
## Multiple R-squared:  0.08637,    Adjusted R-squared:  -0.03545 
## F-statistic: 0.709 on 2 and 15 DF,  p-value: 0.5079

CEST-Age Scatterplot

## [1] "NZMean_52"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1952 -2.0617 -0.5824  1.8089  8.5760 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 12.48210    3.61103   3.457  0.00129 **
## age         -0.09985    0.15683  -0.637  0.52789   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.925 on 41 degrees of freedom
## Multiple R-squared:  0.009789,   Adjusted R-squared:  -0.01436 
## F-statistic: 0.4053 on 1 and 41 DF,  p-value: 0.5279
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3692 -2.8006 -0.7191  1.7114  8.0832 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  14.7066     5.6943   2.583   0.0166 *
## age          -0.1860     0.2461  -0.756   0.4575  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.489 on 23 degrees of freedom
## Multiple R-squared:  0.02423,    Adjusted R-squared:  -0.0182 
## F-statistic: 0.571 on 1 and 23 DF,  p-value: 0.4575
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2250 -1.4023 -0.4332  0.7432  3.3737 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 9.707972   3.784990   2.565   0.0208 *
## age         0.007312   0.165480   0.044   0.9653  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.001 on 16 degrees of freedom
## Multiple R-squared:  0.000122,   Adjusted R-squared:  -0.06237 
## F-statistic: 0.001952 on 1 and 16 DF,  p-value: 0.9653
## [1] "NZMean_54"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4988 -0.9220 -0.2368  0.5849  4.3453 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.26799    1.43617   8.542 2.09e-11 ***
## age         -0.12910    0.06244  -2.068   0.0438 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.317 on 51 degrees of freedom
## Multiple R-squared:  0.07735,    Adjusted R-squared:  0.05926 
## F-statistic: 4.276 on 1 and 51 DF,  p-value: 0.04375
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4217 -1.1132 -0.2611  0.6510  4.2695 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.03377    2.21594   5.882  2.2e-06 ***
## age         -0.16467    0.09606  -1.714   0.0972 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.517 on 29 degrees of freedom
## Multiple R-squared:  0.09201,    Adjusted R-squared:  0.0607 
## F-statistic: 2.939 on 1 and 29 DF,  p-value: 0.09715
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1788 -0.8474 -0.2117  0.4959  2.3121 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.27185    1.67231   6.740 1.48e-06 ***
## age         -0.08224    0.07300  -1.127    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.02 on 20 degrees of freedom
## Multiple R-squared:  0.05967,    Adjusted R-squared:  0.01265 
## F-statistic: 1.269 on 1 and 20 DF,  p-value: 0.2733
## [1] "NZMean_55"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5783 -1.0653 -0.2623  0.8339  2.4316 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.18365    1.65192   5.559 2.48e-06 ***
## age         -0.04220    0.07067  -0.597    0.554    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.279 on 37 degrees of freedom
## Multiple R-squared:  0.009544,   Adjusted R-squared:  -0.01722 
## F-statistic: 0.3565 on 1 and 37 DF,  p-value: 0.5541
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3465 -1.0449 -0.3846  1.1073  2.2402 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.29855    2.34127   3.972 0.000646 ***
## age         -0.05590    0.09969  -0.561 0.580627    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.371 on 22 degrees of freedom
## Multiple R-squared:  0.01409,    Adjusted R-squared:  -0.03072 
## F-statistic: 0.3145 on 1 and 22 DF,  p-value: 0.5806
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42415 -0.71506  0.01333  0.41140  2.10109 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  8.81670    2.22155   3.969   0.0016 **
## age         -0.01200    0.09576  -0.125   0.9022   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.122 on 13 degrees of freedom
## Multiple R-squared:  0.001207,   Adjusted R-squared:  -0.07562 
## F-statistic: 0.01572 on 1 and 13 DF,  p-value: 0.9022
## [1] "NZMean_56"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -2.350 -1.257 -0.245  1.098  3.887 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  11.7609     1.8218   6.456 3.94e-08 ***
## age          -0.1124     0.0792  -1.419    0.162    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.671 on 51 degrees of freedom
## Multiple R-squared:  0.03796,    Adjusted R-squared:  0.0191 
## F-statistic: 2.012 on 1 and 51 DF,  p-value: 0.1621
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4590 -1.6998 -0.4877  1.8290  3.7410 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.1355     2.8694   4.229 0.000214 ***
## age          -0.1242     0.1244  -0.999 0.326125    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.965 on 29 degrees of freedom
## Multiple R-squared:  0.03326,    Adjusted R-squared:  -7.603e-05 
## F-statistic: 0.9977 on 1 and 29 DF,  p-value: 0.3261
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.73509 -0.89526 -0.04446  0.56768  2.16920 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.33770    1.99437   5.685 1.46e-05 ***
## age         -0.10008    0.08706  -1.150    0.264    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.216 on 20 degrees of freedom
## Multiple R-squared:  0.06198,    Adjusted R-squared:  0.01508 
## F-statistic: 1.322 on 1 and 20 DF,  p-value: 0.2639
## [1] "NZMean_60"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11944 -0.53949  0.09176  0.57798  1.90759 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.72366    0.98506   9.871 2.48e-13 ***
## age         -0.09538    0.04272  -2.233   0.0301 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8917 on 50 degrees of freedom
## Multiple R-squared:  0.09066,    Adjusted R-squared:  0.07248 
## F-statistic: 4.985 on 1 and 50 DF,  p-value: 0.03007
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0483 -0.7545  0.1416  0.6548  1.7698 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.82620    1.38851   6.357 6.02e-07 ***
## age         -0.06022    0.06019  -1.000    0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9507 on 29 degrees of freedom
## Multiple R-squared:  0.03336,    Adjusted R-squared:  3.117e-05 
## F-statistic: 1.001 on 1 and 29 DF,  p-value: 0.3254
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51331 -0.60232 -0.09635  0.36032  1.78255 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.92224    1.35202   8.078 1.45e-07 ***
## age         -0.14185    0.05867  -2.418   0.0258 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8006 on 19 degrees of freedom
## Multiple R-squared:  0.2353, Adjusted R-squared:  0.1951 
## F-statistic: 5.846 on 1 and 19 DF,  p-value: 0.02582
## [1] "NZMean_69"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7292 -0.7179 -0.0907  0.5521  3.1234 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.81716    1.26106   8.578 2.53e-11 ***
## age         -0.11751    0.05448  -2.157    0.036 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.106 on 49 degrees of freedom
## Multiple R-squared:  0.0867, Adjusted R-squared:  0.06806 
## F-statistic: 4.652 on 1 and 49 DF,  p-value: 0.03596
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7595 -0.5618 -0.1230  0.4569  2.9568 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.41671    1.62256   7.036 9.72e-08 ***
## age         -0.14143    0.07034  -2.011   0.0537 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.111 on 29 degrees of freedom
## Multiple R-squared:  0.1224, Adjusted R-squared:  0.09209 
## F-statistic: 4.043 on 1 and 29 DF,  p-value: 0.05373
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6543 -0.8413 -0.2978  0.5203  2.2322 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.84379    2.08980   4.710 0.000174 ***
## age         -0.07884    0.08982  -0.878 0.391689    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.145 on 18 degrees of freedom
## Multiple R-squared:  0.04104,    Adjusted R-squared:  -0.01224 
## F-statistic: 0.7703 on 1 and 18 DF,  p-value: 0.3917
## [1] "NZMean_73"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.55301 -0.31899 -0.03307  0.37924  2.00967 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.27841    0.70213  14.639  < 2e-16 ***
## age         -0.08547    0.03045  -2.807  0.00711 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6356 on 50 degrees of freedom
## Multiple R-squared:  0.1361, Adjusted R-squared:  0.1189 
## F-statistic: 7.879 on 1 and 50 DF,  p-value: 0.00711
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5347 -0.3762 -0.1440  0.4106  1.9549 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.78952    1.05990  10.180  4.4e-11 ***
## age         -0.11068    0.04595  -2.409   0.0226 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7257 on 29 degrees of freedom
## Multiple R-squared:  0.1667, Adjusted R-squared:  0.138 
## F-statistic: 5.803 on 1 and 29 DF,  p-value: 0.02257
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.90207 -0.29633 -0.07935  0.38386  0.86686 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.59371    0.79527  12.063 2.37e-10 ***
## age         -0.05124    0.03451  -1.485    0.154    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4709 on 19 degrees of freedom
## Multiple R-squared:  0.104,  Adjusted R-squared:  0.05681 
## F-statistic: 2.205 on 1 and 19 DF,  p-value: 0.154
## [1] "NZMean_74"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29455 -0.55790 -0.02115  0.39419  1.68832 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.87132    0.84898   10.45 4.58e-14 ***
## age         -0.04751    0.03683   -1.29    0.203    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7685 on 49 degrees of freedom
## Multiple R-squared:  0.03284,    Adjusted R-squared:  0.0131 
## F-statistic: 1.664 on 1 and 49 DF,  p-value: 0.2031
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1352 -0.5027  0.1200  0.3581  1.6661 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.580915   1.112832   6.812 2.12e-07 ***
## age         0.004373   0.048270   0.091    0.928    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7618 on 28 degrees of freedom
## Multiple R-squared:  0.000293,   Adjusted R-squared:  -0.03541 
## F-statistic: 0.008205 on 1 and 28 DF,  p-value: 0.9285
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.33207 -0.52996  0.02688  0.36698  1.39466 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.59749    1.24597   8.505 6.67e-08 ***
## age         -0.11656    0.05407  -2.156   0.0441 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7378 on 19 degrees of freedom
## Multiple R-squared:  0.1965, Adjusted R-squared:  0.1543 
## F-statistic: 4.648 on 1 and 19 DF,  p-value: 0.04412
## [1] "NZMean_77"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0155 -0.3010  0.0024  0.4526  1.4179 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.61676    0.75135  11.468 9.85e-16 ***
## age         -0.04764    0.03266  -1.459    0.151    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6891 on 51 degrees of freedom
## Multiple R-squared:  0.04004,    Adjusted R-squared:  0.02122 
## F-statistic: 2.127 on 1 and 51 DF,  p-value: 0.1508
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.03167 -0.33497 -0.00438  0.38447  1.31613 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.575025   1.091884   6.938 1.26e-07 ***
## age         -0.002624   0.047332  -0.055    0.956    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7476 on 29 degrees of freedom
## Multiple R-squared:  0.000106,   Adjusted R-squared:  -0.03437 
## F-statistic: 0.003073 on 1 and 29 DF,  p-value: 0.9562
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.18376 -0.22579  0.05526  0.27598  1.20372 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.93269    0.95997  10.347 1.78e-09 ***
## age         -0.10489    0.04191  -2.503   0.0211 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5853 on 20 degrees of freedom
## Multiple R-squared:  0.2385, Adjusted R-squared:  0.2005 
## F-statistic: 6.265 on 1 and 20 DF,  p-value: 0.0211
## [1] "NZMean_78"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4690 -0.5532 -0.0179  0.8838  3.8966 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.47997    1.52980   4.889 1.13e-05 ***
## age         -0.02893    0.06612  -0.438    0.664    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.351 on 49 degrees of freedom
## Multiple R-squared:  0.003893,   Adjusted R-squared:  -0.01644 
## F-statistic: 0.1915 on 1 and 49 DF,  p-value: 0.6636
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4914 -0.5753  0.1175  0.8742  1.6883 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.81659    1.72334   5.696 4.16e-06 ***
## age         -0.12345    0.07434  -1.661    0.108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.144 on 28 degrees of freedom
## Multiple R-squared:  0.08965,    Adjusted R-squared:  0.05714 
## F-statistic: 2.757 on 1 and 28 DF,  p-value: 0.108
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0612 -1.3203  0.0227  1.0785  3.3541 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  4.48569    2.67607   1.676    0.110
## age          0.09177    0.11596   0.791    0.438
## 
## Residual standard error: 1.559 on 19 degrees of freedom
## Multiple R-squared:  0.03192,    Adjusted R-squared:  -0.01904 
## F-statistic: 0.6264 on 1 and 19 DF,  p-value: 0.4385
## [1] "NZMean_87"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.87144 -0.51472  0.00192  0.41888  1.76781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.80110    0.89752   9.806 3.84e-13 ***
## age         -0.03630    0.03915  -0.927    0.358    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8145 on 49 degrees of freedom
## Multiple R-squared:  0.01724,    Adjusted R-squared:  -0.002819 
## F-statistic: 0.8595 on 1 and 49 DF,  p-value: 0.3584
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.80776 -0.45411  0.00869  0.37145  1.90643 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.19477    1.25151   6.548 5.06e-07 ***
## age         -0.01045    0.05456  -0.192    0.849    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8411 on 27 degrees of freedom
## Multiple R-squared:  0.001358,   Adjusted R-squared:  -0.03563 
## F-statistic: 0.03672 on 1 and 27 DF,  p-value: 0.8495
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.31106 -0.56358  0.07676  0.38116  1.58995 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.53729    1.32375   7.205 5.65e-07 ***
## age         -0.06765    0.05779  -1.171    0.255    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8071 on 20 degrees of freedom
## Multiple R-squared:  0.06413,    Adjusted R-squared:  0.01734 
## F-statistic: 1.371 on 1 and 20 DF,  p-value: 0.2555
## [1] "NZMean_88"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2605 -0.6695 -0.1306  0.4076  2.1013 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.23714    0.94274  10.859 9.34e-15 ***
## age         -0.07578    0.04088  -1.853   0.0697 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8534 on 50 degrees of freedom
## Multiple R-squared:  0.06429,    Adjusted R-squared:  0.04557 
## F-statistic: 3.435 on 1 and 50 DF,  p-value: 0.06972
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0677 -0.5290 -0.1665  0.4151  2.1122 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.52628    1.23968   8.491 2.35e-09 ***
## age         -0.08797    0.05374  -1.637    0.112    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8488 on 29 degrees of freedom
## Multiple R-squared:  0.0846, Adjusted R-squared:  0.05303 
## F-statistic:  2.68 on 1 and 29 DF,  p-value: 0.1124
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.25811 -0.74182  0.01342  0.37613  1.93043 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.85064    1.52172   6.473 3.33e-06 ***
## age         -0.05950    0.06603  -0.901    0.379    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9011 on 19 degrees of freedom
## Multiple R-squared:  0.04098,    Adjusted R-squared:  -0.009491 
## F-statistic: 0.812 on 1 and 19 DF,  p-value: 0.3788
## [1] "NZMean_91"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.27626 -0.45036 -0.04829  0.34549  1.72972 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.54927    0.73477  12.996   <2e-16 ***
## age         -0.04576    0.03194  -1.432    0.158    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6739 on 51 degrees of freedom
## Multiple R-squared:  0.03868,    Adjusted R-squared:  0.01983 
## F-statistic: 2.052 on 1 and 51 DF,  p-value: 0.1581
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.33791 -0.46644  0.04102  0.27926  1.68126 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.68385    1.03639   9.344 3.01e-10 ***
## age         -0.04923    0.04493  -1.096    0.282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7096 on 29 degrees of freedom
## Multiple R-squared:  0.03976,    Adjusted R-squared:  0.006648 
## F-statistic: 1.201 on 1 and 29 DF,  p-value: 0.2822
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1162 -0.4773 -0.1217  0.4867  1.0171 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.40656    1.05804   8.891  2.2e-08 ***
## age         -0.04289    0.04619  -0.929    0.364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6451 on 20 degrees of freedom
## Multiple R-squared:  0.04134,    Adjusted R-squared:  -0.006594 
## F-statistic: 0.8624 on 1 and 20 DF,  p-value: 0.3641
## [1] "NZMean_92"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.98701 -0.68943  0.04373  0.57165  2.73288 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.15208    1.08921   9.321 1.97e-12 ***
## age         -0.13733    0.04707  -2.917  0.00531 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9621 on 49 degrees of freedom
## Multiple R-squared:  0.148,  Adjusted R-squared:  0.1306 
## F-statistic: 8.511 on 1 and 49 DF,  p-value: 0.005314
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9622 -0.6337  0.0397  0.4808  2.7635 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  10.0549     1.3654   7.364  5.1e-08 ***
## age          -0.1343     0.0589  -2.280   0.0304 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9065 on 28 degrees of freedom
## Multiple R-squared:  0.1566, Adjusted R-squared:  0.1264 
## F-statistic: 5.197 on 1 and 28 DF,  p-value: 0.03044
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.78779 -0.84970  0.04414  1.01407  1.56951 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.26990    1.85958   5.523 2.51e-05 ***
## age         -0.14080    0.08058  -1.747   0.0967 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.083 on 19 degrees of freedom
## Multiple R-squared:  0.1385, Adjusted R-squared:  0.09311 
## F-statistic: 3.053 on 1 and 19 DF,  p-value: 0.09672
## [1] "NZMean_93"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4985 -0.2243  0.0570  0.5289  1.0813 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.40032    0.81818  10.267 1.06e-13 ***
## age         -0.05301    0.03537  -1.499     0.14    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7227 on 48 degrees of freedom
## Multiple R-squared:  0.04471,    Adjusted R-squared:  0.0248 
## F-statistic: 2.246 on 1 and 48 DF,  p-value: 0.1405
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4756 -0.2110  0.2318  0.5151  0.9402 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.54054    1.16827   7.310  7.3e-08 ***
## age         -0.05959    0.05042  -1.182    0.248    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7756 on 27 degrees of freedom
## Multiple R-squared:  0.04919,    Adjusted R-squared:  0.01397 
## F-statistic: 1.397 on 1 and 27 DF,  p-value: 0.2476
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47001 -0.23831 -0.07685  0.48753  1.08448 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.21418    1.16794   7.033 1.07e-06 ***
## age         -0.04422    0.05061  -0.874    0.393    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6802 on 19 degrees of freedom
## Multiple R-squared:  0.03863,    Adjusted R-squared:  -0.01197 
## F-statistic: 0.7634 on 1 and 19 DF,  p-value: 0.3932
## [1] "NZMean_96"
## [1] 1
## 
## Call:
## lm(formula = formula_str, data = graph_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3092 -0.5810 -0.0906  0.6523  3.6968 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.61348    1.31281   6.561 5.04e-08 ***
## age         -0.01525    0.05664  -0.269    0.789    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.112 on 44 degrees of freedom
## Multiple R-squared:  0.001646,   Adjusted R-squared:  -0.02104 
## F-statistic: 0.07252 on 1 and 44 DF,  p-value: 0.789
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "PSY", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.17902 -0.51176  0.04751  0.66331  1.74391 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.309072   1.443320   5.757 4.62e-06 ***
## age         -0.007259   0.062384  -0.116    0.908    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9574 on 26 degrees of freedom
## Multiple R-squared:  0.0005204,  Adjusted R-squared:  -0.03792 
## F-statistic: 0.01354 on 1 and 26 DF,  p-value: 0.9083
## 
## 
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group == 
##     "NC", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0449 -0.7819 -0.2820  0.7763  3.5092 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  9.15019    2.57777   3.550  0.00267 **
## age         -0.03037    0.11089  -0.274  0.78771   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.358 on 16 degrees of freedom
## Multiple R-squared:  0.004665,   Adjusted R-squared:  -0.05754 
## F-statistic: 0.07499 on 1 and 16 DF,  p-value: 0.7877